Image descriptor and method of pattern recognition

ABSTRACT

Image description and image recognizable method, it contain (a) It obtain an image which possess plural pixels. (b) It determines a starting position in the image. (c) In the image, From the starting point along the trajectory of the former spiral aggregation makes a pixel sampling, and the pixel on the trajectory rank to the former spiral aggregation.(d) the angle increases with the increase of the variance, it forming a the angle of the latter spiral aggregation. From the starting point along a trajectory of the former spiral aggregation makes the pixel sample, and the pixel on the trajectory rank to the former spiral aggregation. (e) It decides how many frequencies the angle variation increase, and repeatedly performs the step (d). After obtaining a plurality of the latter spiral aggregation, the pixel corresponds to the value. (f) It ranks the former spiral aggregation and the latter spiral aggregation. Then, spiral aggregation map will be formed and recorded the every value of the pixel.

FIELD OF THE INVENTION

The present invention relates to field of image processing, and moreparticularly to a recognizable image in rotation, image description inchange and image recognizable method.

BACKGROUND OF THE INVENTION

Currently, the searching method is applied widely to web andappearance-based recognition, electronic component on circuit board, thelicense plates on the vehicle, building on a blank shot or a telemetrymap and the cell in biological images etc.

As described above, in the images the first describe the particularblock and the target figure. With the particular block and the matchingsimilarity of the target figure, it will search similar or completelyconsistent goal figure in this images. And, the description of the imagecan be directed against color or gray scale images.

Although the color images and gray scale images can be matched. But, thedescription of the gray scale images is less information than the colorit. Therefore, it should ease the operative burden during the Imagematching process.

In the tradition, the gray scale images are two kinds of descriptionmethod, ring Projection Transformation and Radial ProjectionTransformation. As has been said before, two descriptions are wonadvantage, but those are some defect.

For example, the ring conversion had an advantage of the anti-rotation.But, the disadvantage is not applicable in Circular symmetry orradiation from center to outside. The Circular conversion should besampled by a ring shaped manner. Otherwise, it is easy to get to thesame information in the same directions, and loss a radial direction.

In view of the aforesaid, the present invention supplies an imagerecognizable method. There are not only maintain the annular conversionand the advantage of the radial transition, also resist image changesize and find the angle change after rotating around .

SUMMARY OF THE INVENTION

A first aspect of the invention is to provide an image descriptions, itis spiral aggregation track to depict the new spiral aggregation map. Toachieve against the images rotate, move and change.

A second aspect of the invention is to provide an image description, aspreviously stated, with image description establish an image descriptionmodel to identify an image object type.

A third aspect of the invention is to provide an image description. Witha decision of the three steps, the decisive process is simple toplurality of. Image objects filter out a sub image t hat is similar theimage description model.

In order to achieve the above and other objectives, the presentinvention provides to an image description method. It includes (a)Obtaining an image which possess plural pixels. (b) Determining astarting position in the image. (c) In the image, From the startingpoint along the trajectory of the former spiral aggregation makes apixel sampling, and the pixel on the trajectory rank to the formerspiral aggregation.(d) the angle increases with the increase of thevariance, it forming a the angle of the latter spiral aggregation. Fromthe starting point along the trajectory of the former spiral aggregationmakes the pixel sample, and the pixel on the trajectory rank to theformer spiral aggregation. (e) Deciding how many frequency the anglevariation increase, and repeatedly performs the step (d). Afterobtaining a plurality of the latter spiral aggregation, the pixelcorresponds to the value. (f) Ranking the former spiral aggregation andthe latter spiral aggregation. Then, spiral aggregation map will beformed and recorded the every value of the pixel.

In order to achieve the above and other objectives, the presentinvention provides to an image description method. Using an imagedescription model establish an image description model to identify theimage object. The image object has a plurality of pixels, and imagedescription model is provided with spiral aggregation map. The methodcomprises step (a) Image object model based on the size of the imagedescription. Step (b) the center of contrast area is corresponding toone of the pixel. The angle of the spiral aggregation track samples afirst contrast area, the pixel sampled is corresponding to a value.Wherein, the value can be an average of the pixel or come into second orhigher level values. Step (c) Comparing a value and the value of spiralaggregation map, wherein, it forms a numerical distribution. Step (d)Determining the value within a distribution and record the coordinateswhich the center point correspond to pixel. Next, step (e) compared areamoves to next pixel and implement step (d). Until all regions of animage object to complete the scan.

In the other embodiment, after step (d), step (f) Removing the recordedcentral point, to determine the second compared area. Step (g) in thesecond compared are, a plurality of spiral aggregation track samples thepixel. And, it should be a plurality of values. This value can be anaverage of the pixel or come into second or higher level values. Step(h) Comparing the value and spiral aggregation map are corresponding tothe value, among spiral aggregation map contain the value and Intervalnumber of different values frequency. And, step (i) Deciding the valuein interval number of different values, and record that the center pointis corresponding to the pixel coordinates.

In the further embodiment, after step (i) to step (j) Removing therecorded central point, to determine the third compared area. Step (k)In the third compared are, a plurality of spiral aggregation tracksamples the pixel. And, it is one of the pixels in spiral aggregationmap. Step (l) Calculating the projective amount of the verticaldirection in the spiral aggregation map and the compared spiralaggregation map, and Calculating the projective amount in the spiralaggregation map and the pixels. It determines a feature of the points.Step (m) calculating the projective amount of the horizontal directionin the spiral aggregation map and the compared spiral aggregation map.It determines a rotation of the angle. And, step (n) According to thefeature of the points and the rotation of the angle, it determines therelationship between the image description model and spiral aggregationmap.

Compared to conventional technology, the present invention provides toan image descriptive mothed and utilizing the image descriptive mothedexecutes image recognizable method. The image descriptive mothed canretain the structural of the image and continuous information. Becausethe image descriptive model contain more subtle information aboutimages. Therefore, it retains the simultaneously advantages of anannular projection transformation and a radial projectiontransformation. To achieve against the images rotate, move and change.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is the flow diagram about the image according to an embodiment ofthe present invention.

FIG. 2 explains FIG. 1 image schematic diagram according to anembodiment of the present invention.

FIG. 3 explains FIG. 1 spiral aggregation map according to an embodimentof the present invention.

FIG. 4 contrast diagram about the image description method with spiralaggregation map according to an embodiment of the present invention.

FIG. 5 is the flow diagram about the image description method accordingto an embodiment of the present invention.

FIG. 6 is another flow diagram about the image description methodaccording to an embodiment of the present invention.

FIG. 7 is flow diagram about the image description method according toan embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

As indicated above, the invention provides a scenario-based securitymethod and system. The following comprises preferred embodiments of theinvention, which describe different aspects of the present invention.

Referring to in FIG. 1 that is the flow diagram about the imagedescription method of the present invention. In FIG. 1, Imagedescriptive method begins from step S11. As shown in FIG. 2, it obtainsan image which plural pixels.

Step S12, determine a starting position in the image. For example, thestaring position is general on the center of the image.

Step S13, in the image, from the starting point along the trajectory ofthe former spiral aggregation makes a pixel samples, and the pixel onthe trajectory rank to the former spiral aggregation.

In the specific examples, spiral aggregation definition is the dot onthe spiral aggregation track. From one point has a fixed speed to thenext point. It stars from the center point until the periphery finished.The spiral aggregation refers Archimedes screw geometry theorems. Withthe projection method formed spiral aggregation track, and it establishthe image descriptive. In the characteristic of the spiral aggregation,when spiral aggregation extends outward from the center, itsdisplacement in horizontal and vertical direction will be conducted atthe same time. With the projection in the horizontal direction andvertical direction can understand the structure of the image component.

Step S14, the angle increases with the increase of the variance, itforming an angle of the latter spiral aggregation. From the startingpoint along the trajectory of the former spiral aggregation makes apixel sample, and the pixel on the trajectory rank to the former spiralaggregation. For example, the variance range is between 0.5 and 1degree.

Step S15, deciding how many frequency the angle variation increase, andrepeatedly performs the step S14. It obtains a plurality of the latterspiral aggregation. For example, the frequent range is between land 720times.

By repeating Step S14 and Step S15, the former spiral aggregation andthe latter spiral aggregation rank sequentially and vertically in spiralaggregation map, as shown in FIG. 3. For example, spiral aggregation mapis a rectangle. The former spiral aggregation and the latter spiralaggregation form side of the rectangles, and another side of rectangleis ranked from those pixel sampling. The detailed content will bedescribed as blow.

Step S16, arranging the former spiral aggregation and the latter spiralaggregation and form the spiral aggregation map. In other embodiment,the former spiral aggregation and the latter spiral aggregation ranksequentially and vertically in spiral aggregation map.

In FIG. 3, spiral aggregation map is made from every pixel which are ondifferent angles of spiral aggregation track. The spiral aggregation mapis defined as Sam(i, φ). Function indicates that the i-th pixel in thetrajectory of the φ spiral aggregation, and spiral aggregations are onthe basis of the φ order to rank it. In FIG. 3, horizontal direction ismade from all pixels i in one spiral aggregation track. The range ofpixel i is between land p times. Wherein, P represents the total numberof samples for the one spiral aggregation

In addition, if spaced distribution rate of every spiral aggregation isΔ φ angles. Thus, spiral aggregation map will get 2π/Δφ spiralaggregation in total.

Referring to FIG. 4, there is the contrast diagram about the image withspiral aggregation map. In FIG. 4, the two Spiral aggregations are wortha concern. One is φ that the vertical line of the zero degree, anotheris φ that the vertical line of the π/2 degree (the 90 degree). Thespiral aggregation indicated that the horizontal direction of spiralaggregation map. If we take a vertical line in the vertical direction,it indicates that after each rotation of the spiral aggregation, thei-th pixel of the pooled. In conclusion, if the combination of all thevertical lines forms annular projective amount of the image. Accordingto the definition of the geometry spiral theorem, there is the samespiral sampling. So, spiral aggregation will not be affected the trackwhich is made of spiral aggregations by change the size of the image.

In conclusion, spiral aggregations have two important features. First,it can resist to image size change. Because, when the size is changed,the image will maintain almost the same appearance in the verticalprojection of spiral aggregations Map. Second, the rotation can bepredicted. For example, after the image is rotated, will show thedirection of the vertical displacement on spiral aggregations Map. Withthe displacement of the change quantity and direction, it can find outthe differences in the direction and angle between the rotated image andthe does not rotate image.

Referring to FIG. 5 is the flow diagram about the image description ofthe present invention. In FIG. 5, image recognizable method supplies theimage descriptive method to establish the image descriptive model andidentify the image object. It is a plural pixel, and the imagedescriptive model possesses the spiral aggregations Map. The method starfrom step S51. Image object based on the size of the image descriptivemodel and decide the size in the first compared area.

Step S52, the center of compared area correspond the one of the pixels,and the spiral aggregations angle of the trajectory samples the pixel inthe first contrast area. The sampling pixel is corresponding to a value.

Step S53, comparing the value and the value of spiral aggregation map.The value of spiral aggregation map forms a numerical distribution.

Step S54, deciding whether the value falls the range of the valuesdistribution, and recode the center point corresponding to thecoordinates of the pixel.

Step S55, the contrast area is moved to the next pixel, and it executesStep S54 until all the areas of the image object to complete the scan.

FIG. 6 is another flow diagram about the image description of thepresent invention. After step S55 to step S61. By obtaining the recordedcentral point, it determines the second compared area.

Step S62, in the second contrast area, with the trajectory of the spiralaggregations samples the pixel. The pixels is corresponding the pluralvalues. It's worth that each spiral aggregations present sparse shape.The spiral aggregation which was selected is a representative and itsimage with a general description. For example, the number of spiralaggregation is 4. The two adjacent spiral aggregations differ from the90 degree.

Step S63, comparing the value and the value of the spiral aggregations.The Spiral Aggregations Map includes a value and a set of intervalnumber of different values.

Step S64, deciding the value in interval number of different values, andrecord that the center point is corresponding to the pixel coordinates.

FIG. 7 is flow diagram about the image description of the presentinvention. After step S64 to step S71. By obtaining the recorded centralpoint, it determines the third compared area. It determines the thirdcompared area.

Step S72, in the third contrast area, the intensive plurality of spiralaggregations in the trajectory samples the pixel in the contrast area.In order to receive a projective among of the pixel and form the one ofspiral aggregation map. It is worth to understand that each spiralaggregations present dense shape, and the image can be completelydescribed. For example, the number of spiral aggregation is 360. The twoadjacent spiral aggregations differ from a predetermined angle. Forexample, this angle is 1 degree

Step S73, calculating the projective amount of the vertical direction inthe compared spiral aggregation map and the spiral aggregation map, andcalculating the projective amount of pixel and the spiral aggregationmap. It determines the points of the feature. It can decide the centerposition in the image.

Step S74, calculating the projective amount of the horizontal directionin the compared spiral aggregation map and the spiral aggregation map.It determines the rotation of the angle.

Step S75, according to the points of the feature and the angle of therotation, it decides the relationship between the image descriptivemodel and spiral aggregations Map.

The present invention is disclosed above by preferred embodiments.However, persons skilled in the art should understand that the preferredembodiments are illustrative of the present invention only, but shouldnot be interpreted as restrictive of the scope of the present invention.Hence, all equivalent modifications and replacements made to theaforesaid embodiments should fall within the scope of the presentinvention. Accordingly, the legal protection for the present inventionshould be defined by the appended claims.

What is claimed is:
 1. An image descriptive method comprises: (a)obtaining an image, wherein the image having a plurality of pixels; (b)determining a starting point in the image; (c) sampling the plurality ofpixels from the starting point along a trajectory of a former spiralaggregation with a former angle in the image, and arranging theplurality of pixels on the trajectory to form a former spiralaggregation; (d) forming a latter spiral aggregation with a latter angleafter increasing an angle variation in the former angle, and samplingthe plurality of pixels form the starting point along the trajectory ofthe latter spiral aggregation, and arranging the plurality of pixels onthe trajectory to form a latter spiral aggregation; (e) deciding afrequency of the increased variance for repeatedly performing the step(d) for obtaining a plurality of the latter spiral aggregation, whereinthe plurality of pixels correspond to a value; and (f) arranging theformer spiral aggregation and the latter spiral aggregation form aspiral aggregation map, and recording the value of each the plurality ofpixels.
 2. An image descriptive method of claim 1, wherein a range ofthe frequent is between 1 and 720 times.
 3. An image descriptive methodof claim 2, wherein a range of angle variation is between 0.5 and 1degree.
 4. An image descriptive method of claim 1, wherein the startingpoint is located at a center of the image.
 5. An image descriptivemethod of claim 1, wherein in step (f), the former spiral aggregationand the latter spiral aggregation are arranged sequentially andvertically to the spiral aggregation map.
 6. An image recognizablemethod for utilizing an image descriptive method to establish an imagedescriptive model that identify an image object with a plurality ofpixels, wherein the image descriptive model with a spiral aggregationmap, the method comprises: (a) determining a size of a first comparedarea according to a size of the image descriptive model in the imageobject; (b) a center point of first compared area corresponding to oneof the plurality of pixels, and a trajectory of a spiral aggregationwith an angle sampling the plurality of pixels in the first comparedarea, wherein the plurality of pixels corresponding to a value; (c)comparing the value and a value of spiral aggregation map, wherein thevalue of the spiral aggregation map to form a numerical distribution;(d) determining the value within the numerical distribution forrecording a coordinate of the pixel correspond to the center point offirst compared area; and (e) The pixel from the first compared areamoving to the next pixel and executing step (d) until completely scanentire region of the image object.
 7. An image recognizable method ofclaim 6, wherein after step (e) further comprises: (f) obtaining therecorded center point to determine a second compared area; (g) samplingthe plurality of pixels located at the second compared area by atrajectory of the plurality of spiral aggregation in the second comparedarea, wherein the sampling pixel corresponding to a plurality of value;(h) comparing the value and a value of spiral aggregation map, whereinthe spiral aggregation map includes a value and an interval number ofdifferent values; and (i) deciding the value in the interval number ofdifferent values to record for recording a coordinate of the pixelcorrespond to the center point.
 8. An image recognizable method of claim6, wherein after step (g), the number of spiral aggregation is 4, andthe two adjacent spiral aggregations differ from a 90 degree.
 9. Animage recognizable method of claim 7, wherein after step (i) furthercomprises: (j) removing the recorded center point to determine a thirdcompared area; (k) sampling the plurality of pixels located at the thirdcompared area by a trajectory of the plurality of spiral aggregation inthe third compared area to obtain a projective amount of the pluralityof pixels or form a compared spiral aggregation map; (l) calculating aprojective amount of the spiral aggregation map and the compared spiralaggregation map in vertical direction in the compared spiral orcalculating the projective amount of the pixel and the spiralaggregation map to determine the points of the feature; (m) calculatinga projective amount of the spiral aggregation map and the comparedspiral aggregation map in a horizontal direction to determine a rotationof the angle; and (n) determining a relationship between the imagedescription model and the spiral aggregation map according to thefeature of the points and the rotation of the angle.
 10. An imagerecognizable method of claim 9, wherein in step (k), the number ofspiral aggregation is 360, the two adjacent spiral aggregations differfrom a predetermined angle.